Legged robots have become capable of performing highly dynamic maneuvers in the past few years. However, agile locomotion in highly constrained environments such as stepping stones is still a challenge. In this paper, we propose a combination of model-based control, search, and learning to design efficient control policies for agile locomotion on stepping stones. In our framework, we use nonlinear model predictive control (NMPC) to generate whole-body motions for a given contact plan. To efficiently search for an optimal contact plan, we propose to use Monte Carlo tree search (MCTS). While the combination of MCTS and NMPC can quickly find a feasible plan for a given environment (a few seconds), it is not yet suitable to be used as a reactive policy. Hence, we generate a dataset for optimal goal-conditioned policy for a given scene and learn it through supervised learning. In particular, we leverage the power of diffusion models in handling multi-modality in the dataset. We test our proposed framework on a scenario where our quadruped robot Solo12 successfully jumps to different goals in a highly constrained environment.
@article{arxiv.2403.03639,
title = {Diffusion-based learning of contact plans for agile locomotion},
author = {Victor Dhédin and Adithya Kumar Chinnakkonda Ravi and Armand Jordana and Huaijiang Zhu and Avadesh Meduri and Ludovic Righetti and Bernhard Schölkopf and Majid Khadiv},
journal= {arXiv preprint arXiv:2403.03639},
year = {2025}
}